{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "                                                   0\n",
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       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/yanghongyi/Desktop/第一章：数据科学原理与数据处理【314120】数据科学原理与数据处理流程/模块四 第一章/28.代码/log.txt',header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>/front-api/bill/create</td>\n",
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       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
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       "      <td>60</td>\n",
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       "      <td>162808</td>\n",
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       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
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       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/yanghongyi/Desktop/第一章：数据科学原理与数据处理【314120】数据科学原理与数据处理流程/模块四 第一章/28.代码/log.txt',header = None,sep='\\t' )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2019162542</td>\n",
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       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
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       "      <td>60</td>\n",
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      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>api</th>\n",
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       "      <th>count</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_min</th>\n",
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       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>res_time_sum</th>\n",
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       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>844483</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1049.72</td>\n",
       "      <td>102.80</td>\n",
       "      <td>180.96</td>\n",
       "      <td>131.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-09 20:43:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133833</th>\n",
       "      <td>9920113</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>93.41</td>\n",
       "      <td>93.41</td>\n",
       "      <td>93.41</td>\n",
       "      <td>93.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-09 00:58:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88171</th>\n",
       "      <td>6713361</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2122.05</td>\n",
       "      <td>103.69</td>\n",
       "      <td>322.59</td>\n",
       "      <td>192.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-11 22:56:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45321</th>\n",
       "      <td>3854005</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1941.02</td>\n",
       "      <td>69.64</td>\n",
       "      <td>441.43</td>\n",
       "      <td>194.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-23 20:41:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111531</th>\n",
       "      <td>8253232</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>933.35</td>\n",
       "      <td>90.75</td>\n",
       "      <td>240.84</td>\n",
       "      <td>155.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-14 14:39:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            api                      id  count  res_time_max  res_time_min  \\\n",
       "7607     844483  /front-api/bill/create      8       1049.72        102.80   \n",
       "133833  9920113  /front-api/bill/create      1         93.41         93.41   \n",
       "88171   6713361  /front-api/bill/create     11       2122.05        103.69   \n",
       "45321   3854005  /front-api/bill/create     10       1941.02         69.64   \n",
       "111531  8253232  /front-api/bill/create      6        933.35         90.75   \n",
       "\n",
       "        created_at  res_time_avg  interval         res_time_sum  \n",
       "7607        180.96         131.0        60  2018-11-09 20:43:25  \n",
       "133833       93.41          93.0        60  2019-04-09 00:58:24  \n",
       "88171       322.59         192.0        60  2019-02-11 22:56:06  \n",
       "45321       441.43         194.0        60  2018-12-23 20:41:43  \n",
       "111531      240.84         155.0        60  2019-03-14 14:39:58  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) #随机采样，多次执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "api               int64\n",
       "id               object\n",
       "count             int64\n",
       "res_time_max    float64\n",
       "res_time_min    float64\n",
       "created_at      float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "res_time_sum     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   api           179496 non-null  int64  \n",
      " 1   id            179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   res_time_sum  179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 磁盘占用情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.794960e+05\n",
       "mean     6.877739e+06\n",
       "std      6.012494e+06\n",
       "min      1.626440e+05\n",
       "25%      3.825233e+06\n",
       "50%      6.811510e+06\n",
       "75%      9.981455e+06\n",
       "max      2.019163e+09\n",
       "Name: api, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.000000\n",
       "mean        359.880374\n",
       "std         638.919827\n",
       "min          36.550000\n",
       "25%         198.280000\n",
       "50%         256.090000\n",
       "75%         374.410000\n",
       "max      142468.270000\n",
       "Name: created_at, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 179496 entries, 177.72 to 298.97\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   api           179496 non-null  int64  \n",
      " 1   id            179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   res_time_sum  179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index #变成时间类型索引 唯一性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>api</th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>created_at</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>res_time_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [api, id, count, res_time_max, res_time_min, created_at, res_time_avg, interval, res_time_sum]\n",
       "Index: []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2019162542,     162644,     162742, ...,   13438917,   13438981,\n",
       "         13439086])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.api.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                     api  count  res_time_sum  res_time_min  \\\n",
       "0       2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1           162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2           162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3           162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4           162943  /front-api/bill/create      3        568.89        138.45   \n",
       "...            ...                     ...    ...           ...           ...   \n",
       "179491    13438800  /front-api/bill/create     11       2783.48         99.24   \n",
       "179492    13438866  /front-api/bill/create     10       1951.10         85.37   \n",
       "179493    13438917  /front-api/bill/create      3        494.17        103.95   \n",
       "179494    13438981  /front-api/bill/create      9       1798.28        101.11   \n",
       "179495    13439086  /front-api/bill/create      6       1017.97         74.45   \n",
       "\n",
       "        res_time_max  res_time_avg           created_at  \n",
       "0             177.72         132.0  2018-11-01 00:00:07  \n",
       "1             240.38         149.0  2018-11-01 00:01:07  \n",
       "2             225.73         169.0  2018-11-01 00:02:07  \n",
       "3             196.61         145.0  2018-11-01 00:03:07  \n",
       "4             232.02         189.0  2018-11-01 00:04:07  \n",
       "...              ...           ...                  ...  \n",
       "179491        489.90         253.0  2019-05-30 23:06:21  \n",
       "179492        529.51         195.0  2019-05-30 23:07:21  \n",
       "179493        211.47         164.0  2019-05-30 23:08:21  \n",
       "179494        433.30         199.0  2019-05-30 23:09:21  \n",
       "179495        298.97         169.0  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('id',axis = 1)\n",
    "df.drop('api',axis = 1)\n",
    "df.drop('interval',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0      8       1057.31         88.75        177.72         132.0   \n",
       "1      5        749.12        103.79        240.38         149.0   \n",
       "2      5        845.84        136.31        225.73         169.0   \n",
       "3      9       1305.52         90.12        196.61         145.0   \n",
       "4      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id','api','interval'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   count         179496 non-null  int64  \n",
      " 1   res_time_sum  179496 non-null  float64\n",
      " 2   res_time_min  179496 non-null  float64\n",
      " 3   res_time_max  179496 non-null  float64\n",
      " 4   res_time_avg  179496 non-null  float64\n",
      " 5   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30)\n",
    "plt.show() #大部分在十次以内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据，绘制一天时段的借口调用情况\n",
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重采样\n",
    "df2 = df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[['count']].resample('1H').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vkqFUnWzg2TUHnS7Fb33/5S0UFJfzw3mj+fSFA5wuxyuunZBNSnw0DwdJe5euLGPwD2AVMMIYc9gYcyfwc+AyY8wuYI777yIi4mVnmgdPHeL/b1nvqgvzkpmS15+Hlu+hvkmjUGerqK3n+cJD3H7RID43bbDT5XhNTGQ4n5s2iOU7K9lWGvjrgXXlXXg3W2szrbWR1toca+3j1tqj1tpLrbXDrLVzrLVan19ExMsCrXmwJ7586XAqaxt4TqNQH/PqhiO0Wrh1SnCMPLV164UDiY0KjvXAtBK5iIifCsTmwV01Ja8/kwf15y8ahfqYRUUlnJed5FfNgb0lKTaSmycP4NWNRzh8LLCbDCtAiYj4qUBsHtxVxhi+PGcY5ScaeKEwuBZY7Imd5bVsPXKCaydkO12Kz9wxfTAGeGLlfqdL6REFKBERPxWozYO76qIhyUwa2I8/v7uHhmaNQgG8tL6E8DDD1eMDd8mCzmT37cPV47J4bu1Bjp8K3CbDClAiIn4okJsHd5Uxhi9fOozSmnpeXHfY6XIc19pqeWVDCTOGpZASH+10OT61cGYepxpbePrDwF0PTAFKRMQPBXrz4K66eFgKEwb05c/L9tDYHDxtPrrjw31HKa2p59qJgb3mU1eMzEhk1ohU/vbB/oCdA6cAJSLihwK9eXBXGWP40qXDKDl+mpfWh/Yo1KL1JcRHRwTVml/ncs+MIVSdbOSl9SVOl9ItClAiIn4mWJoHd9Ws4amMy0nij8t20xREzWY9cbqxhX9vKePKsRlBt2RFR6bk9ef8nCQefW8vLa2B195FAUpExM8ES/PgrjozCnX42GkWBehoRE8t3lbOyYZmrp0YvO++O9uZJsP7qupYXBx4TYYVoERE/EwwNQ/uqtkj0zgv2zUK1RyCo1CL1h8mMymGKYODZ8X5rrhibAYD+sfylwBsMqwAJSLiR4KteXBXnRmFOlh9ipc3HHG6nF5VWdvAil1VXDM+O6T+zeE/TYY3HjrOmn2B1dREAUpExI8EY/PgrpozKo3RmYn8cemukBqFem3jEVpaLdeF0O27thZMyiE5LoqHA6y9iwKUiIgfCcbmwV11ZhRq/9FTvLYpdEahFhWVMCYrkeHpwde6pStiIsO57aJBLN1ewc7yWqfL6TIFKBERPxHMzYO7au7odEZmJPDg0t0B+c4sT+2uqGVzSU1Qt27pis9MGUifyMBqMqwAJSLiJ4K5eXBXhYW5RqH2VtbxegiMQr20voQwQ1C3bumKfnFRfOqCXF7ZUEJpzWmny+kSBSgRET8RzM2DPXHFmAyGp8cH/SiUq3XLES4elkpaQozT5TjuzumDabXw1/f3O11KlyhAiYj4iWBvHtxVYWGG+2cPY3fFSd7cXOp0OT6zZn81JcdPh+zk8bPl9o/lk+dl8uzqg9ScbnK6nE4pQImI+IFQaB7siU+cl8nQtHgeXLqL1iAdhVq0voS4qHDmjs5wuhS/sXBGHicbmnl29UGnS+mUApSIiB8IlebBXRUeZrh/9lB2lp/kra2Bt0p1Z+qbWnhzcylXjM2kT1RovmGgPWOzk7h4WApPvL+Phmb/bjKsACUi4gdCpXmwJ646P4u81Dj+sCT4RqHe2VZObUNzyL/7rj33zBhCZW0DLxf5d1sfBSgREYeFWvPgrjozCrW9rPajEbpgsWh9CemJ0SG53ldnpg1NZkxWIg+v2OvXwVkBSkTEYaHWPNgT887PYlByLH9YsivgeqV15OjJBpbvrGT++GzCQ6x1S1cYY7hn5hD2Vtbxzjb/Dc4KUCIiDgvF5sFdFREexn2zh1FceoJ3tlU4XY5XvLbxCM2tlmv17rsOfWJsBjn9+vj1wpoKUCIiDgrV5sGemD8+iwH9Y3lgyc6gGIVaVFTCqMxERmYkOl2K34oID+Ou6YMpPHCMwv3+2WRYAUpExEGh3Dy4qyLCw7jvkqFsKTnB0u2BPQq1p/IkGw/XcJ0mj3fqxgty6Rsb6bdNhhWgREQcFMrNgz1x7cRscvr1Cfi5UC8XuVq3XBPirVu6IjYqgs9OHcTi4nJ2V5x0upyPUYASEXGImgd3XWR4GF+8ZCgbD9fw7s5Kp8vpltZWy6KiEqYNTSEtUa1buuK2qQOJjgjjUT8chVKAEhFxiJoHe+b6iTlk9+3DA+8E5ihU4YFjHD6m1i2eSI6P5sb8XBYVlVB+ot7pcv6LApSIiEPUPNgzURFhfH7WEDYcOs57u6qcLsdji4oOExsVzuVj1LrFE3ddPJjm1la/azKsACUi4hA1D/bcgvwcMpNieCDA5kLVN7Xw+qZSLh+TQWxUhNPlBJSByXFcOTaTZz48QG29/zQZVoASEXGAmgd3T3REOF+YNYR1B47xwZ6jTpfTZUu3V1Bbr9Yt3bVwRh61Dc08t+aQ06V8RAFKRMQBah7cfQvyc0lPjA6ouVAvrS8hLSGaaUNTnC4lII3L7cvUvGQeX7mPxuZWp8sBFKBERByh5sHdFxMZzudnDmHN/mpW7fX/Uajqukbe3VHBNeOz1LqlB+6ZmUfZiXpe3XjE6VIABSgRkV6n5sE9d9PkAaQmRPOHJbucLqVTr29yt26ZkON0KQFt5vBURmYk8MiKPX7RZFgBSkSkl6l5cM/FRIZz78whfLi3mtV+Pgq1qKiEkRkJjM5S65aecDUZzmNn+Une3en8ivQKUCIivUzNg73j05MHkBIfzR+W+u8o1L6qOooOHtfkcS+56vwsspJieGi58wtr9ihAGWO+aozZaozZYoz5hzFGS6uKiJyDmgd7T5+ocO6Zkcf7u4/6bcPZRUUlGAPXjFeA8obI8DDumD6YNfuqWX/wmKO1dDtAGWOygS8B+dbasUA4cJO3ChMRCUZqHuxdt0wZQHJcFA/44Vwoay0vF5UwbUgKGUkaX/CWmyYPIDEmgkccHoXq6S28CKCPMSYCiAX8Y2q8iIifUvNg74qNiuDuGXm8t6uKdQecHZE427oDxzhYfUq377wsPjqCz0wdyNvFZeyrqnOsjm4HKGttCfBr4CBQCtRYawvO3s4Ys9AYU2iMKaysDMwGkCIi3qDmwb7xmSkD6R8X5XfvyHupqISYyDAuH6vWLd5220WDiAgzPLVqv2M19OQWXj/gGmAwkAXEGWNuPXs7a+0j1tp8a21+ampq9ysVEQlwah7sG3HREdx18WCW76xkw6HjTpcDuOa6veFu3RIfrdYt3paWEMNlo9N5ZcMRxxbW7MktvDnAPmttpbW2CXgJuMg7ZYmIBB81D/adz04dRN/YSL8ZhVq2vYKa0026fedDC/Jzqa5rZOn2ckfO35MAdRCYYoyJNcYY4FJgm3fKEhEJPmoe7Dvx0RHcNX0wS7dXsOmw86NQL60vISU+mulq3eIzM4alkp4YzfOFhx05f0/mQK0GXgTWA5vdx3rES3WJiAQVNQ/2vc9eNIjEmAj+sGS3o3Ucq2tkmbt1S0S4llv0lfAww/UTc3h3RwXlJ+p7/fw9+pe11v7QWjvSWjvWWvsZa22DtwoTEQkmah7se4kxkdw5PY93tpWzpaTGsTre2FxKU4vV7btesCA/l1brGvHrbYrGIiK9QM2De8ft0waREBPh6FyoRUUlDE+PZ4xat/jc4JQ4LhjUjxcKD2Ft7/bHU4ASEfExNQ/uPUl9IvnctMEUFJfz4JJdvd509sDROtYdOMa1E3JwTQ8WX1uQn8veqrpeXwdMAUpExMfUPLh3fWHWEOaPz+I3i3dy79PrqK1v6rVzn2ndMn9CVq+dM9R98rxMYqPCeaGXJ5MrQImI+JiaB/eumMhwfvep8Xz/qtEs2V7B/D+9z+6Kkz4/r7WWRUUlTM1LJjOpj8/PJy5x0RF88rxMXt90hLqG5l47rwKUiIgPqXmwM4wx3Dl9ME/feSHHTzUx/0/v8/bWMp+ec/3B4xw4eor5mjze6268IJe6xhbe3Fzaa+dUgBIR8SE1D3bW1CHJvHb/dIakxnHP39fx67d30OKjeVGLig4THRHGlWrd0uvyB/ZjcEocL6zrvdt4ClAiIj6k5sHOy+rbh3/eM5Ub83P447Ld3PnkWmpOeXdeVGNzK69vKmXumAwSYiK9emzpnDGGGyblsGZfda81GFaAEhHxETUP9h8xkeH84vrz+d/5Y3l/dxVX/2kl28tOeO34y3ZUcPxUE9fp9p1jrp+YQ5iBF9cd6pXzKUCJiPiImgf7F2MMt04ZyHMLp3K6sYVr//QBr2084pVjL1pfQkp8FBcPU+sWp2QkxTBzeCr/Wlfis9u0bSlAiYj4iJoH+6dJA/vx+v3TGZOVyP3/KOKnbxTT3NLa7ePVnGpi6fYK5o1T6xanLcjPpexEPe/tqvT5ufQvLSLiI2oe7L/SEmN49u4pfHbqQB59bx+ffWIN1XWN3TrWG5tLaWxp5boJOV6uUjx16ag0+sVG9sqaUApQIiI+oObB/i8qIoyfXDOWX91wPoUHjjHvwZVsPux5D71FRYcZmhbP2Gy1bnFadEQ48ydks7i4nGPdDMRdpQAlIuIDah4cOBbk5/LivVOx1nL9Qx/wogdvhT9UfYq1+49x7YRstW7xEwsm5dLY0sorG3zbYFgBSkTEB9Q8OLCcn9OX1+6fzqQB/fj6Cxv5wStbaGzufF7UoiLXf9JaPNN/jM5KZGx2Is/7+DaeApSIiJepeXBgSo6P5u93Tubuiwfz1KoD3PLYh1TU1ne4/ZnWLRcO7k92X7Vu8Sc35udSXHqCLSWe35LtKgUoEREvU/PgwBURHsb3PjmaB24az+aSGq76w0rWHTjW7rYbDh1nX1Ud103U6JO/uXpcFlHhYR7djvWUApSIiJepeXDgu2Z8Nou+MI2YyHBuemQVz6w+gLX/vbbQoqISV+uW8zIdqlI60jc2irlj0nl5QwkNzS0+OYcClIiIF6l5cPAYlZnIq/dNY+qQFL63aAvf/tdm6ptc/xk3Nrfy2sYjzBmdTqJat/ilG/NzOX6qiXeKK3xyfAUoEREvUvPg4NI3Noq/3n4BX7xkCP8sPMSnHvmQI8dPs3xnJcfUusWvTRuaQmZSDM8X+qa1S4RPjioiEqLUPDj4hIcZvnH5SM7L7svXnt/AvAdXktOvD8lxUcwYnup0edKB8DBXg+E/LttNac1pMpO8O9FfI1AiIl7S2mpZXFzOrBFqHhyMrhibwSv3TSMpNpKNh2uYNy6LSLVu8Ws3TMrBWnhpvffXhNK/vIiIl2w8fJyK2gbmjs5wuhTxkaFpCbz8xWl8fe5wvjBriNPlSCcGJsdx4eD+PF946GNvAugpBSgRES8pKC4nIsxwyQg1Dw5miTGR3Dd7GGmJMU6XIl1wY34uB46eYs2+aq8eVwFKRMRLCraWcWFef5Ji9a4sEX9x5XkZxEdH8IKX14RSgBIR8YI9lSfZU1mn23cifiY2KoKrzs/kjU2lnGxo9tpxFaBERLxgsZoHi/itBfm5nG5q4Y1NR7x2TAUoEREvKNhaxnnZSWSpJ5qI35k4oC9DUuN4wYsNhhWgRER6qOJEPUWHjmvxTBE/ZYxhQX4uhQeOsafypFeOqQAlItJD72yrwFqYO0bzn0T81XUTsgkPM14bhVKAEhHpoYLiMgb0j2V4erzTpYhIB9ISY7hkRCovrT9Mc0trj4+nACUi0gO19U18sPsoc0enY4yaB4v4sxsm5VJR28CKXZU9PpYClIhIDyzfWUljS6tu34kEgNkj00iOi+L5tT2/jacAJSLSA4uLy+kfF8Wkgf2cLkVEOhEVEca1E7JZsr2coycbenQsBSgRkW5qbG5l6fYK5oxKIzxMt+9EAsGC/FyaWiwvb+jZmlAKUCIi3bR631Fq65u1+rhIABmRkcC4nCRe6GGD4R4FKGNMX2PMi8aY7caYbcaYqT05nohIICnYWk6fyHCmD0txuhQR8cCC/Fy2l9WypeREt4/R0xGoB4C3rLUjgXHAth4eT0QkILS2WhYXlzNjeAoxkeFOlyMiHpg3LovoiDCeLzzU7WN0O0AZY5KAGcDjANbaRmvt8W5XIiISQDaX1FB2ol6370QCUFKfSK4Ym8ErG0qob2rp1jF6MgI1GKgE/mqMKTLGPGaMiTt7I2PMQmNMoTGmsLKy5+suiIj4g8XF5SUYj4sAAB19SURBVISHGWaPTHO6FBHphhvzczlR30yBuxG4p3oSoCKAicBfrLUTgDrg22dvZK19xFqbb63NT01N7cHpRET8R0FxGZMH9adfXJTTpYhIN0zNSya7bx9e6OZtvJ4EqMPAYWvtavffX8QVqEREgtq+qjp2lp9k7hg1DxYJVGFhhhsm5bBydxUlx097vn93T2ytLQMOGWNGuB+6FCju7vFERALF4uIyAC4brQAlEshumJSDtfCvdZ6vTN7Td+HdDzxjjNkEjAd+1sPjiYj4vYKt5YzOTCSnX6zTpYhID+T2j+WiIcm8sO4Qra2erQnVowBlrd3gnt90vrV2vrX2WE+OJyLi7yprG1h38Jhu34kEiRvzczlUfZoP9x31aD+tRC4i4oGl28uxFi1fIBIkrhibQUJMBC8WenYbTwFKRMQDBVvLyenXh1GZCU6XIiJeEBMZzrxxWby5pZQT9U1d3k8BSkSki+oamnlvdxVzR2dgjJoHiwSLG/NzqW9q5fWNpV3eRwFKRKSLVuyspLG5Ve++Ewky43KSGJ4ezwvrur4mlAKUiEgXFRSX0zc2kgsG9XO6FBHxImMMCyblUnTwOLvKa7u0jwKUiEgXNLW0smRbOZeOTCciXD86RYLN/AnZRIQZXujimlD6KSAi0gVr91Vzor5ZyxeIBKnUhGhmj0zjpfUlNLW0drq9ApSISBcUFJcTExnGjGHq6SkSrBbk51J1soF3d1R2uq0ClIhIJ6y1FGwt4+JhqfSJCne6HBHxkVkjUkmJj+b5LjQYVoASEenE1iMnOFJTr3ffiQS5yPAwrp+YzbLtFVTWNpxzWwUoEZFOFGwtI8zApSPTnC5FRHxsQX4Oza2Wl4tKzrmdApSISCcKisvJH9Sf5Phop0sRER8bmpbAhAF9O72NpwAlInIOB4+eYntZLXN1+04kZNyYn8uuipPn3EYBSkTkHAqKywA1DxYJJVedn0lM5LkjkgKUiMg5FBSXMzIjgQHJsU6XIiK9JCEmktsuGnTObRSgREQ6cPRkA4X7q3X7TiQEfefKUed8XgFKRKQDS7ZX0Gph7hjdvhOR/6YAJSLSgYKt5WQlxTAmK9HpUkTEzyhAiYi043RjCyt3VzJ3TAbGGKfLERE/owAlItKOFbsqqW9q1fwnEWmXApSISDsKtpaT1CeSCwb3d7oUEfFDClAiImdpbmllyfZyZo9MIzJcPyZF5OP0k0FE5Cxr9x/j+Kkm3b4TkQ4pQImInKWguIyoiDBmDE91uhQR8VMKUCIibVhrKdhazsVDU4iLjnC6HBHxUwpQIiJtbCutpeT4aeaO0e07EemYApSISBsFxWUYA7NHKkCJSMd6NUAdO9XYm6cTEfFYwdZyJg3oR2pCtNOliIgf69UAVVZTT11Dc2+eUkSkyw5Vn6K49IRu34lIp3o1QDW3Wp5ctb83Tyki0mWLi8sBuGy0mgeLyLn1aoBKiIng4eV7OVHf1JunFRHpkoLiMoanxzM4Jc7pUkTEz/VqgEpPjKHmdBOPv7evN08rItKpY3WNrN1/jLkafRKRLujVANUnMpwrx2bw+Mp9HKvThHIR8R9Lt1fQ0mq5TKuPi0gX9PoyBl+9bDh1jc08tGJPb59aRKRDBcVlZCTGcF52ktOliEgA6HGAMsaEG2OKjDGvd2X74ekJXDMuiyc/2E9FbX1PTy8i0mOnG1tYvrOSy0anExZmnC5HRAKAN0agvgxs82SHr8wZTlOL5c/LNAolIs5bubuK+qZWLV8gIl3WowBljMkBPgk85sl+g1LiWDAph2dXH+TI8dM9KUFEpMcKtpaREBPBhYOTnS5FRAJET0egfg98E2j1dMf7Lx0GwINLd/ewBBGR7mtptSzZXsHskWlERai7lYh0Tbd/WhhjrgIqrLXrOtluoTGm0BhTWFlZ+dHj2X37cPPkXF4oPMSBo3XdLUNEpEfWHThGdV2j3n0nIh7pya9b04CrjTH7geeA2caYp8/eyFr7iLU231qbn5qa+l/PffGSoYSHGR54Z1cPyhAR6b6CrWVEhYcxc3hq5xuLiLh1O0BZa79jrc2x1g4CbgKWWmtv9eQYaYkx3HbRIBZtKGFXeW13SxER6RZrLQXF5Vw0NJmEmEinyxGRAOL4Df97ZuQRGxnO7zUKJSK9bEd5LQerT2n1cRHxmFcClLX2XWvtVd3ZNzk+mjumD+aNzaVsPVLjjXJERLqkYGs5xsCc0WlOlyIiAcbxESiAuy7OIzEmgt8t3ul0KSISQhYXlzMhty9pCTFOlyIiAcYvAlRSn0jumTmEd7ZVUHTwmNPliEgIOHL8NJtLarhMt+9EpBv8IkAB3H7RIPrHRfFbjUKJSC9YXFwOoNXHRaRb/CZAxUVH8IVZQ3hvVxUf7j3qdDkiEuQKissYkhrHkNR4p0sRkQDkNwEK4NYpA0lLiOY3BTuw1jpdjogEqZpTTXy4t5q5Y3T7TkS6x68CVExkOPfPHsra/cdYsavK6XJEJEgt3VFOS6tlrlYfF5Fu8qsABXDjBblk9+2jUSgR8ZnFxeWkJUQzLqev06WISIDyuwAVHRHOly8dxqbDNR9N8hQR8Zb6phbe3VHJnNHphIUZp8sRkQDldwEK4LqJ2QxOieO3i3fS2qpRKBHxng/2VHGqsUW370SkR/wyQEWEh/GVOcPYXlbLG5tLnS5HRILIy0VHiI+OYOqQZKdLEZEA5pcBCmDe+VmMSE/gd4t30tzS6nQ5IhIEVu6q4tWNR7hlygCiI8KdLkdEApjfBqiwMMNXLxvO3qo6FhWVOF2OiAS4uoZmvv3SJvJS4vjqnOFOlyMiAc5vAxTA5WPSGZudyANLdtHYrFEoEem+X761nZLjp/nlDecTE6nRJxHpGb8OUMYYvjZ3BIePneb5wkNOlyMiAWrNvmqeXHWA26YOIn9Qf6fLEZEg4NcBCmDW8FQmDezHg0t3Ud/U4nQ5IhJgTje28M0XN5Lbvw/fvGKE0+WISJDw+wBljOHrc0dQfqKBZ1YfdLocEQkwv3tnJ/uPnuLn151PbFSE0+WISJDw+wAFMHVIMtOGJvPnZbupa2h2uhwRCRBFB4/x2Ht7uXnyAKYNTXG6HBEJIgERoAD+57IRHK1r5G8f7He6FBEJAA3NLXzjxU2kJ8bw3U+MdLocEQkyAROgJg3sx+yRaTy8fA81p5ucLkdE/NyDS3azu+IkP7vuPBJiIp0uR0SCTMAEKID/uWw4J+qbeXzlPqdLERE/tqWkhr8s38P1E3O4ZESa0+WISBAKqAA1NjuJK8dm8MTKfVTXNTpdjoj4oaaWVr7x4ib6x0Xx/atGOV2OiASpgApQAF+9bDh1jc08vGKP06WIiB/6y7t72FZ6gp/OH0vf2CinyxGRIBVwAWp4egLXjMviyQ/2U1Fb73Q5IuJHdpTV8uDSXcwbl8XcMRlOlyMiQSzgAhTAV+YMp6nF8udlGoUSEZfmlla++eJGEmIi+dG80U6XIyJBLiAD1KCUOBZMyuHZ1QcpOX7a6XJExA88vnIfGw/X8OOrx5AcH+10OSIS5AIyQAHcf+kwAP64dJfDlYiI0/ZUnuQ3i3cyd3Q6V52f6XQ5IhICAjZAZfftw82Tc3m+8DD7q+qcLkdEHNLSavnWi5voExnO/84fizHG6ZJEJAQEbIAC+OIlQ4kIM/xhiUahRELVU6v2U3jgGD+4ajRpiTFOlyMiISKgA1RaYgy3XTSIRRtK2FVe63Q5ItLLDh49xS/f2sGsEalcNzHb6XJEJIQEdIACuHfmEGIjw/n9OxqFEgkl1lq+9a9NhIcZfnbtebp1JyK9KuADVP+4KO6cPpg3NpeypaTG6XJEpJf8Y80hVu09yvc+OYqsvn2cLkdEQkzAByiAOy/OIzEmgv/v9WItayASAkqOn+Znb25j2tBkbrog1+lyRCQEBUWASuoTyTcuH8Ga/dVM/8VSbntiDW9tKaWppdXp0kTEy6y1fPelzbS0Wn5+3fm6dScijohwugBv+czUQVwyMo3n1x7i+cLD3Pv0elLio7lhUg43XZDLoJQ4p0sUES/41/oSlu+s5EfzRpPbP9bpckQkRBlrba+dLD8/3xYWFvr8PM0trSzfWck/1hxi2Y4KWlotU/OSuWlyLleMzSA6ItznNYiI91WcqGfOb5czIiOBfy6cSliYRp9ExHeMMeustfntPdftEShjTC7wFJAOWOARa+0D3T2eN0WEh3HpqHQuHZVO+Yl6Xig8xHNrD/Hl5zbQLzaS6ybmcPPkXIamJThdqoh0kbWW7728hYbmVn5x/fkKTyLiqG6PQBljMoFMa+16Y0wCsA6Yb60t7mif3hqBak9rq+X9PVU8t+YQBcVlNLVY8gf246bJA/jkeZn0idKolIg/e3XjEb70jyK++4mRLJwxxOlyRCQEnGsEymu38IwxrwB/tNYu7mgbJwNUW1UnG3hp/WGeW3OIvVV1JMREMH98NjdNzmVMVpLT5YnIWY6ebOCy360gt38sL33+IsI1+iQivcDnAcoYMwhYAYy11p4467mFwEKAAQMGTDpw4ECPz+ct1lrW7KvmubWHeGNzKY3NrZyfk8RNFwzg6vFZxEcHzRx7kYB237PreXtrGW986WKGp+vWu4j0Dp8GKGNMPLAc+Km19qVzbesvI1DtOX6qkUVFJTy35hA7ymuJjQpn3vlZ3DQ5l/G5ffVWaRGHvLWljHufXsfXLhvO/ZcOc7ocEQkhPgtQxphI4HXgbWvtbzvb3p8D1BnWWooOHee5NQd5bWMpp5taGJmRwM2TBzB/fDZJsZFOlygSMo6fauSy360gNT6aV+6bRmR4UCxdJyIBwicByriGZJ4Eqq21X+nKPoEQoNqqrW/i1Y1HeG7NITaX1BAdEcaTd0xmSl6y06WJhIT/eX4Dr244wiv3TdP8RBHpdT5ZxgCYBnwG2GyM2eB+7LvW2jd7cEy/khATyS0XDuSWCweypaSGF9cd5rxs/RAX6Q3Ltlfw0voS7p89VOFJRPxOtwOUtXYlEDITg8ZmJzFW4UmkV5yob+K7izYzPD2e+2YPdbocEZGP0YQCEfE7//fmdspP1PPLG8apc4CI+CUFKBHxK+/vruIfaw5y98V5jM/t63Q5IiLt0kJHIuKYU43N7CirZVtpLdtKT7Ct9ARbjtQwOCWOr1423OnyREQ6pAAlIj5nraW0pv6jkHQmMO07WseZNwLHR0cwMiOBBZNyuX3aIGIidetORPyXApSIeFVDcwu7yk9+FJSKS2vYXlbL8VNNH22T278PozISmTcui1GZiYzOTCSnXx81CBaRgKEAJSLdVlnb0GZUyRWY9lSepLnVNawUExnGiIxErhybwajMREZlJjIyI4GEGC1IKyKBTQFKRLrEWsvmkhre2lLG5pIatpXWUnWy4aPnM5NiGJWZyJzRaR+FpUHJcWr8KyJBSQFKRM7pUPUpXtlQwqKiEvZU1hERZhiensDM4amMykxgtDss9YuLcrpUEZFeowAlIh9z/FQjb2wu5eWiEtbuPwbA5MH9ueviPD4xNlM9IUUk5ClAiQgA9U0tLNtewaKiEpbtqKCpxTI0LZ5vXD6Ca8ZnkdMv1ukSRUT8hgKUSAhrbbWs2V/Ny0UlvLG5lNr6ZlITorlt6iDmT8hmTFYirr7hIiLSlgKUSAjaWV7LoqISXikq4UhNPbFR4VwxNoNrJ2Rz0ZAUTfwWEemEApRIiCg/Uc+rG46wqKiE4tIThIcZZgxL4VtXjuSy0enERunHgYhIV+knpkgQO9nQzFtbyni5qIT391RhLYzL7cuP5o3mqnFZpMRHO12iiEhAUoASCTJNLa28t6uSRUVHWFxcRn1TKwP6x3L/7GHMH59FXmq80yWKiAQ8BSiRIPLiusP835vbOFrXSL/YSBZMymX+hGwmDuiryeAiIl6kACUSRFLio5gyJJlrx2czY3gqURFhTpckIhKUFKBEgsisEWnMGpHmdBkiIkFPv56KiIiIeEgBSkRERMRDClAiIiIiHlKAEhEREfGQApSIiIiIhxSgRERERDykACUiIiLiIQUoEREREQ8pQImIiIh4SAFKRERExEPGWtt7JzOmFtjRaycMTElAjdNF+Dldo3PT9emcrlHndI06p2t0bsFwfUZYaxPae6K3e+HtsNbm9/I5A4ox5hFr7UKn6/BnukbnpuvTOV2jzukadU7X6NyC4foYYwo7ek638PzPa04XEAB0jc5N16dzukad0zXqnK7RuQX19entW3iFGoESERGRQHCu3NLbI1CP9PL5RERERLqrw9zSqyNQIiIiIsFAc6B8yBhzhTFmhzFmtzHm2+7HHjfGbDTGbDLGvGiMie9g3++499thjLn8XMcMZB1cI2OM+akxZqcxZpsx5ksd7HubMWaX++O2No9PMsZsdh/zD8YY01uvxxc6uEazjTHrjTFbjDFPGmPafUNIKFwjY8wTxpgKY8yWNo/9yhiz3f19tsgY07eDfdv9fjLGDDbGrHY//k9jTFRvvBZf6eAa/cgYU2KM2eD++EQH+4byNRpvjPnQfX0KjTGTO9g3FL7Pco0xy4wxxcaYrcaYL7sfX+D+e6sxpsMpOkH5dWSt1YcPPoBwYA+QB0QBG4HRQGKbbX4LfLudfUe7t48GBruPE97RMZ1+rT64Rp8DngLC3NultbNvf2Cv+89+7s/7uZ9bA0wBDPBv4EqnX6sPrtEhYLh7m58Ad4bwNZoBTAS2tHlsLhDh/vwXwC+6em3dzz0P3OT+/CHg806/Th9cox8BX+/O118IXaOCM98bwCeAd9vZL1S+zzKBie7PE4Cd7p9Fo4ARwLtAfih9HXV7BKqD34q7lCRNaIyuTAZ2W2v3WmsbgeeAa6y1J8A1ygL0Adq7h3oN8Jy1tsFauw/Y7T5eu8fshdfiKx29ns8DP7HWtgJYayva2fdyYLG1ttpaewxYDFxhjMnEFVI/tK7vyKeA+b3xYnykvWt0PdBord3p3max+7GzhcQ1stauAKrPeqzAWtvs/uuHQE47u7b79ef+3pwNvOje7kkC+PpA+9eoi0L9Glkg0f15EnCknV1D5fus1Fq73v15LbANyLbWbrPWdra+Y1B+HXUrQBljwoE/AVfiSqA3G2NG4/pN73fW2qHAMeDOdvYdDdwEjAGuAP5sjAk/xzEDVTauUYIzDrsfwxjzV6AMGAk86H7samPMTzrZt8NjBqiOXs8Q4FPuIfN/G2OGARhj8o0xj3Wyb7b787MfD1Ttvc4MIKLNcPkNQC6E7DXqzB24fvvHGJNljHnT/XhH1ycZON4mgAXz9bnPfZvzCWNMP9A1OstXgF8ZYw4Bvwa+A/o+M8YMAiYAq8+xTdB/HXV3BKqjkYOuJMlQGV3pkLX2c0AWrgT/Kfdjr1prf+BoYf4jGqi3rreOPgo8AWCtLbTW3uVoZf7B4vol5HfGmDVALdACukZnM8Z8D2gGngGw1h6x1rY71ycE/QXXLyvjgVLgN6BrdJbPA1+11uYCXwUeh9D+PjOuebv/Ar5y5o5Ke0Lh66i7AaqjNNlukgzR0ZUS3KMCbjnuxwCw1rbwn9sxXd33nMcMQB29nsPAS+7HFgHne7BvCf99uyYor5G1dpW19mJr7WRgBa75CF3al+C7Ru0yxtwOXAXc4r6FcraOrs9RoK/5z8T8oLw+1tpya22L+1b5o7h+iT1bSF8j4Db+87PoBTy7RkH3fWaMicQVnp6x1r7U2fZtBOXXUa+8Cy9ER1fWAsPc88KicI0YvGqMGQofzYG6Gtjezr6vAjcZY6KNMYOBYbgmI7Z7zF54Lb7S0et5GbjEvc1M2g8HbwNzjTH93Lce5gJvW2tLgRPGmCnua/xZ4BVfvxAf6ujrKA3AGBMNfAvX5Muzhco1+hhjzBXAN4GrrbWnOtis3WvrDlvLcN0aBdd/okF1fQDcc3TOuBbY0s5mIX2NcM15mun+fDawq51tQuL7zP0aHge2WWt/6+Huwfl11J2Z58BUXF8gZ/7+HfdHFf9558t/bXP2tm3+/rZ723aP2Z36/OUD17s2duJ698H3cAXW94HNuH5YPYP7XXm4wtRP2uz7Pfd+O2jzzo2zj+n0a/T2NXI/1hd4w32dVgHj3I/nA4+12fcOXLeAdwOfa/N4vvv67gH+iHu9s0D96OAa/QrXLeAduIbS2772kLpGwD9w3YJqwjV6eaf79R4CNrg/HnJvmwW8ea5r6348D9cvLbtxjTxEO/06fXCN/u7+HtuE6xeXTF2jj12j6cA6XO8aWw1Mcm8bit9n03FNH9jU5vvqE7jC92GgASjH/f94KHwddWshTfdw207gUlzDbWuBT+N6W+y/rLXPGWMeAjZZa/981r5jgGdxDYVmAUtwjbCY9o5prd3qcYEiIiIiPtStW3jWNc/pPlyjR9uA591B51vA/xhjduOaXf84/PccKPd2zwPFwFvAF63rPnxHxxQRERHxK2rlIiIiIuIhtXIRERER8ZAClIiIiIiHFKBEREREPNTlANVB77v73H+3xpiUc+z7rjHmoHsdiTOPvWyMOdmz8kVERER6X5cC1Dn61L0PzAEOdOEwx4Fp7uP1xdXZWURERCTgdHUEqt0+ddbaImvt/i4e4zlcq48CXMd/lsfHGBNvjFlijFlvjNlsjLnG/fhPjDFfabPdT40xX+7i+URERER8oqsByht96pYAM9yjWTcB/2zzXD1wrbV2Iq4WHr9x3+57Atfy9xhjwtz7Pe3heUVERES8KqLzTbymBViJKwT1sdbubzslCviZMWYG0IornKW7tzlqjJkApANF1tqjvViziIiIyMd0NUB11Em5XcaYt3EFnkJr7V1tnnoOWISr5UtbtwCpuPoMNRlj9gMx7uceA24HMnCNSImIiIg4qqsB6qNOyriC0024et+1y1p7eQdPvQf8H66mjW0lARXu8HQJMLDNc4uAnwCR5zqniIiISG/p0hyojvrUGWO+ZIw5jGtEapMx5rFOjmOttb+21lad9dQzQL4xZjOuOU/b2+zTCCxzn7Olqy9MRERExFf8vheee/L4emCBtXaX0/WIiIiI+PVK5O61pnYDSxSeRERExF/4/QiUiIiIiL/x6xEoEREREX+kACUiIiLiIQUoEREREQ8pQImIiIh4SAFKRAKKMWaWMeaibuy33xiT0o39vuvpPiIS/BSgRMQxxpju9OOcBXgcoHpAAUpEPqY3mwmLSAgyxnwW+DpggU24GovXAxOA940xfwL+hKsf5ingbmvtdmPMPOD/AVHAUVw9M/sA9wItxphbgftxdS54CBjgPuVXrLXvG2OScbWNygZW4Wpafq46X8bV8zMGeMBa+4gx5udAH2PMBmCrtfYWb1wTEQl8WgdKRHzGGDMGVz/Li6y1VcaY/sBvgRTgGmttizFmCXCvtXaXMeZC4P+stbONMf2A49Zaa4y5Cxhlrf2aMeZHwElr7a/d53gW+LO1dqUxZgDwtrV2lDHmD0CVtfYnxphPAq8Dqe20kjpTa39rbbUxpg+u/p8zrbVHjTEnrbXxvrxOIhJ4NAIlIr40G3jhTGhxBxTcj7UYY+Jx3Y57wf04QLT7zxzgn8aYTFyjUPs6OMccYHSb/RPdx50BXOc+7xvGmGOd1PolY8y17s9zgWG4Rr5ERD5GAUpEnFDn/jMM1yjT+Ha2eRD4rbX2VWPMLOBHHRwrDJhira1v+2CbQNUp9/HnAFOttaeMMe/iupUnItIuTSIXEV9aCixwz0fCfQvvI9baE8A+Y8wC9/PGGDPO/XQSUOL+/LY2u9UCCW3+XoBrLhTuY5wJYyuAT7sfuxLod446k4Bj7vA0EpjS5rkmY0xkZy9UREKLApSI+Iy1divwU2C5MWYjrvlPZ7sFuNP9/FbgGvfjP8J1a28d0Hbe0mvAtcaYDcaYi4EvAfnGmE3GmGJck8wBfgzMMMZsxXUr7+A5Sn0LiDDGbAN+DnzY5rlHgE3GmGe6+rpFJPhpErmIiIiIhzQCJSIiIuIhTSIXkZDhnou1pJ2nLrXW6h13ItJluoUnIiIi4iHdwhMRERHxkAKUiIiIiIcUoEREREQ8pAAlIiIi4qH/HwodNPKJ4TXdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 折线图和直方图可以看到业务的高峰时段\n",
    "plt.figure(figsize = (10,5))\n",
    "df2['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析异常 使用箱线图\n",
    "plt.figure(figsize = (10,6))\n",
    "df['2019-5-1'][['count']].boxplot(showmeans = True,meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析某一天的响应时间\n",
    "plt.figure(figsize = (10,5))\n",
    "df['2019-5-5']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-5-1']\n",
    "df2[df['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "data = df['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周末与平时\n",
    "df['2019-5-1'].index.weekday\n",
    "df['weekday'] = df.index.weekday\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t/QnDjqZ2zfbANRMlo5zoDpHnETZXlSb0ep+9BD6RqoTxJfRNFc78lbeQVNVD0QCurmKM4dM/PkltwMebf2vDos978Y4a7rl5Ew/8up1Hjvckfd7+0SkujU07mtpVWVpInkc0FzzDnOgJs6W6lIK8xEPQ1hp/QkMobi2hnyu+hqHX5Y6DBnB1lZ+e7ONQ+zDvffHWZasBfuDl26kpK+QHR7uTPu/s9lYOjk16PUJNmU9zwTPMie5Q0kMY22pKOT84zlRk5UN4bi6hn8uX76W8OJ9el3fm0QCurhCLGT7941M0VRTzu/sbln1+vtfDvsZyjnQMJ33uEy6NTdYFfXRpDzxjjIzP0DUyyY4kf1Fvq/ETjZlVTaS7uYR+PiuVUCcxVQr957EeWrpD/PFLt5K/wupwexoDXBgcZ3h8Oqlzn+gJUxvwESwuSOo489UGdDVmJjnR48wv6suZKCufyHRzCf181SnYmUcDuLrC5x9rY1tNKbdft/LqcHsbrEJERzuS2zyh1YE/qxdSG/TRMzJJLKaLeTKBU8XKNlWV4JHVba/m9hL6udaXub+cXgO4mtUXnuRkb5jX729c1QTPrgZr/8kjFxMfRpmOxDjd5/z+hAB1gSKmozEGxtxf2qyWd6InzLqSAqqTLFbmy/fSXFGyqolMt5fQz1VT5qM/PEUkybrlS0kqgIvIn4jIcRE5JiLfEBGfUw1TqfdMuxWA920oX9Xrynz5bKoq4UgSPfAz/aNEYibpcdGFzOaC62KejNDaE2bHen9CS+jn21pTuqqiVm4voZ+rpsxHzMDgWHJDi0tJOICLSD3wPmC/MWYX4AXe6FTDVOodah8m32vtJblaexuCHOkYTrj2Q3xcNNGl1UuJ54LrYp70i8YMp3qcy/XfVuPn/ODYihaTpWIJ/Vyzi3lcnH9JdgglDygSkTygGHC+OIZKmcPtQ1xbF0hoI+E9DQH6w1MJrzxr7Q5TkOfcEvq54j1wXU6ffu2XxpmYiTo2Br21xk/MwNn+5TNRUrGEfq5ULOZJOIAbYzqBTwPtQDcwYox5xKmGqdSKRGMc7RhZ1c4oc+1ptF535GJiwyit3SG21ZSueF/E1VhXUkBhnkd74Bkgnip6jWM9cGslZ1vf8uPgrQ5lv6zUbD2UsHtzL8kMoZQDdwAbgTqgRER+b4Hn3S0iB0TkQH9/f+ItVa460RNmYia66vHvuGtry8jzCEcTzAc/4eCf1fOJCLUBny6nzwCtPWE8Yo1dO2FjZQlej6xoIrO12/0l9HNVlBbi9YirqzGT6e68FDhnjOk3xswADwIvmP8kY8x9xpj9xpj9VVVVSZxOuemwnUGyrzGxHrgv38uOWn9CC3oGRqfoD0+52jOy6oJrDzzdTnSH2FhZktAw3UIK87w0VxSvKBf8RHeYbS4voZ/L6xGqSt1NJUwmgLcDzxeRYrGmk18CtDrTLJVqh9uHqCwtpKG8KOFj7GkIcrRjZNX51vHFFU7VAF9IbVCX02eCEz1hxzONttX4l80FN8bQ2hPi2hRNYMbVlBW6WpEwmTHwXwPfAQ4Bz9rHus+hdqkUO9w+zPUbgkmldu1tCBKejKy6yH48A2W7iz3wukARvaFJV3Ny1dLCkzO0Xxp3PNNoa42fC5fGl8xE6Q1NMTw+k5IVmHPVlPnoc3E5fVIzRsaYvzLG7DDG7DLGvMUYoyslstDQ2DTnBsYSHv+O29NoL+hZ5TBKa3eYan8hFaXJLexYSm3Qysntc3FCSS0tPk7tdBDdVlOKMdZagsU4tfpztWrKfJnZA1drxzPx8e8EM1DitlSVUpTvXXUmSmt3yJUFPHPVBTQXPN1mq006tN9p3LYaq0e/1IKe1hT8lbeQ9QEfIxMzrm0CrgFccah9CK9H2GMviU9UntfD7vrAqjJRZqLxJfTufrBqg5oLnm4nekL4fXnUBZxdsN1cUULeMpkord3WEvpAkftL6OeKlwtwaxhFA7jicPswO9b7KS64eued1drTEOB4V4iZFY41x/dGdCoveDG12gNPuxPdYa5Zn9gu9EuJLwBbKhPlRHcoZSsw53J7azUN4DkuGjM8c3E46eGTuD2NQaYiMU72rKzAUGuKqsOV+fIoLczTHniaxDdScOv7vK3Gv+hinsmZKGcHxlI+/g3WEAq4txpTA3iOO903yuhUhH2NyU1gxsVLy650IrO1O0y+V9hU6czCjsXEF/NoDzw9OoYmGJ2KuJYFsrWm1FqmP331WPPpvlGiMZPyDBSAGr8GcOWiw+1DAFzf5EwAb1xXRHlxPkdXOJF5oifElmp/UnsjrlRtUDd2SJcT8V3oXeyBL5aJ0jKbgZL6IZSyojx8+R7XAnjyg54qqx1uHyZYnE9zRbEjxxMRdtuVCVeitTvEjZsrHTn3cuoCPlq6Qik5l7pSvAbK9hq3Arj1F9w7v3aAgjwPM9EYM1FDJBZjfCqKL9+TsiX0c4mInUroziSmBvAcd/jiEPsak1vAM9/ehgD/+NN+xqcjS06MXhqbpjc0lZLdUcCayBwYnWIqEqUwz5ml3GplWntCNFUUU1LoTsjZVFnKXS9oZmB0inyvhzyPkJ/nId8j5Hs97GkMpmwJ/Xw1Lm6tpgE8h4UmZ2jrG+U1e+ocPe6ehiAxA8e7Qjy3ed2iz7u8N2JqxibjqYS9I1NscOgvDrUyJ7rDrta68XiEv759p2vHT0ZNmS/hIm/L0THwHHbk4jDGrH4HnuXMrshcZou1eA2UVGUHxBfzdOlEZkpNTEc5NziWlknETFDjtwpaJbrZyVI0gOeww+3DiMB1jckt4Jmv2u+jLuBbdou11u4QlaUFVCW5N+JKxXvgmomSWqd6wxiTnknETLA+4GNyJkZoMuL4sTWA57BD7UNsq/bjd2GDV6sy4TI9cBdrgC9ktgeuueApNbtdXhrysDNBtYs782gAz1HGGA63O7eAZ749jQEuDI4zPL7whq6RaIxTve6Oi85XVOAlWJxPl9YFT6nW7jDFBV4ay3Nz3sHNrdU0gOeocwNjjEzMuBbALy/oWXgY5fzgGFORWMp7ZbUBzQVPtRM9Ibav9+NJUxZIum2pLuVjr93F5irnF6tpAM9Rh9rjFQidncCM22UXxjq6yERmvDJdqlII4+oCPu2Bp9DsEvoUVwHMJOtKCnjzbzVRF0x8s5TFaADPUYfbh/AX5rHFhV4BQJkvn01VJYv2wE/0hPB6hC3V7i6hn0935kmt0ESE4fEZ10sl5CoN4DnqcPswezcEXf2zdm9DkKfPX+JLT57lqTODjEzMzD7W2h1mc1VJyhfU1AaKGJmYYXza+YwAdbWO4XEA6pPYqk8tThfy5KCxqQgnekL84Yu2uHqe1+yt4+enB/joDy9vlbphXTE768o41D7ELdtSv8l13Zy64Knu/eeiziFruKreheEDpQE8Jx3tGCFmYJ9DBawW86Lt1fzmwy+lPzzF8a4RjneFaOkKcbxrhOHxGZ6/qcLV8y9kbl1wDeDu67TnG7QH7g4N4DnoeJc1Lr2n3tkFPIup8hdy6/Zqbt1ePXvfdCSWkgqE881uraa54CnROTSBL99DRUlBupuyJukYeA66MDhOmS+PdWn8UKUjeAPUBKxVn7qcPjU6hiaoDxY5vguPsmgAz0HnB8dorizJyQ9VYZ6XytJC7YGnSOfwBPU5uoAnFTSA56ALg+NpqY2cKeqCPu2Bp0jn8IROYLpIA3iOmY7E6BgaZ2MOl1O1tlbTHrjbxqcjXBqbpkEnMF2jATzHdAyNEzPkeA+8iO7hCVfKe6rL4iteNYC7RwN4jrkwaC2saK7M3R54XaCIsemoK+U91WUXNQfcdRrAc8y5gTEAmnO4B651wVNjdhGP9sBdowE8x1wYHMNfmN4UwnSr1VzwlOgcniDPI1T7feluypqVVAAXkaCIfEdETohIq4jc4FTDlDvOD47TVFmckymEcbPL6bUH7qrOoQnqgkVp20w4FyS7EvNzwMPGmDtFpADI3YHVLHFhcIydKVqBmamq/T68HtEeuMs6hsZ1/NtlCffARSQA3AzcD2CMmTbGuLP1snLETDTGxaEJNubw+DeA1yPU+Au1B+4yaxGPBnA3JTOEshHoB74iIodF5EsiktuRIcN1Dk0QjRmacjgHPK42WKQ9cBdNR2L0hae0B+6yZAJ4HnA98M/GmH3AGPDn858kIneLyAEROdDf35/E6VSyzg/aGSiV+nu2Llg0WylPOa97ZAJjNAPFbckE8A6gwxjza/v/38EK6FcwxtxnjNlvjNlfVZX6+s/qsvOaQjhrw7oiuoYnmInG0t2UNSmeQqiLeNyVcAA3xvQAF0Vku33XS4AWR1qlXHF+cJySAi+VpbmbQhjXVFFCJGZmA41yVkc8gAd1uM5NyWahvBd4wM5AOQu8LfkmKbdcGByjqSI3qxDOt9EeRopXZlTO6hieQATWBzQH3E1JBXBjzDPAfofaolx2fnCca1K8C3ymik/kxksLKGd1Dk1Q4/elre57rtCrmyMi0RgXL43r+LetqrSQ4gLv7MSuclbn8LhOYKaABvAc0TU8SSRmNIDbRISmihLtgbukc3hCJzBTQAN4joj3NDUH/LLmimLtgbsgGjN0D09qDngKaADPEfFAtVEn7GY1V5Zw8dI4EU0ldFRvyPprT4dQ3KcBPEecHxinKN9Llb8w3U3JGM0VxcxEje7O47D4AintgbtPA3iOsFIIc7sK4XzxXYl0GMVZlxfx6HCd2zSA54hzg2M6gTlP82wA14lMJ2kPPHU0gOeAaMxYKYQ6/n2Fan8hvnwPFwa0B+6kjqFxKkoKKCrwprspa54G8Bxg1fwwNGsGyhU8HqFpXYkOoTisY0jLyKaKBvAcEM91zuWd6BfTXFmsQygO6xye0OGTFNEAngPOzZaR1R74fM0VJbQPjhONmXQ3ZU0wxioQpot4UkMDeA64MDCGL99DjW4ue5WmihKmozF6QppK6ISB0WmmIjHtgaeIBvAccH5wnKZ1JXh0c9mrxOcFdCLTGbMZKJpCmBIawHPAeTsHXF2tyc7MOacTmY6I54BrDzw1NICvcdGYoX1wXJfQL6K2zCp5qkWtnNE5bF1HzUJJDQ3ga1xPaJLpaEwzUBZhpRIWz243p5LTOTSB35dHoCg/3U3JCRrA17jL+2DqEMpitKysczqGNIUwlTSAr3GzZWR1CGVRzRXFXLg0RkxTCZOmdcBTSwP4GndhcJyCPA+1ZZpCuJimyhImZ2L0hafS3ZSs16k98JTSAL7GnR8Yo2ldsaYQLiE+vHROx8GTMjIxQ3gqolUIU0gD+Bp33t6JXi0uXpXwgqYSJmU2hVCHUFJGA/gaFosZLgyOs1GX0C+pLlhEvle0JkqSOobsFEIdQkkZDeBrWG94kqmIphAux+sRGtcVaw88SZdXYWoATxUN4GvYudkUQg3gy2muKNEeeJI6hybw5XuoKClId1NyhgbwNexyGVkdQllOU4XVAzdGUwkTFS8jq9v2pY4G8DXs/OAYBV4PdTomuayNlSWMT0fp11TChHUOT2gRqxTTAL6GXRgYp3FdEV5NIVxWk+6PmTRdhZl6GsDXsPO6kfGKxXPBdXu1xIxPR7g0Nq2rMFMs6QAuIl4ROSwiP3CiQcoZ0Zjh7MCYViFcofpgEXke0UyUBHXZGSgawFPLiR74HwGtDhxHOahjaJzpSIytNaXpbkpWyPN6aCgv4vyADqEkokPrgKdFUgFcRBqAVwFfcqY5yiltvaMAbKn2p7kl2aOpQneoT1SHrsJMi2R74J8FPgjEHGiLctDp/ngA1x74Sm2stMrKairh6p3tt/ZdrdZ9V1Mq4QAuIq8G+owxB5d53t0ickBEDvT39yd6OrVKp/tGqfIXamH9VWiqKGZ0KsLg2HS6m5J12vrCbKvxa8ZTiiXTA78RuF1EzgP/DrxYRP51/pOMMfcZY/YbY/ZXVVUlcTq1Gm19o2zV3veqaFGrxJ3ssQK4Sq2EA7gx5kPGmAZjTDPwRuC/jDG/51jLVMKMMZzpG9Xhk1WKr1jViczVGRqbpi88xXYN4CmneeBrUE9oktGpiPbAV6mhvBiPaC74ap3qDQOwbb0G8FTLc+IgxpjHgcedOJZK3uk+awJzswbwVVzxyk8AABfFSURBVCnI89BQXqyrMVcpHsC1B5562gNfg+IphFs1hXDV4kWt1Mqd7A1T5sujpqww3U3JORrA16DT/aMEivKpLNWynqvVXFHCuQGtSrgap3pG2b7er1UI00AD+Bp0utfKQNEP1Oo1VRQTnowwPD6T7qZkBWMMJ3s1AyVdNICvQaf7NQMlUfFUwnM6jLIifeEpRiZm2K4TmGmhAXyNGRyd4tLYtAbwBG2ssgJ4fCJYLe1kj52Boj3wtNAAvsbEA48G8MQ0V5RQUuDleOdIupuSFWZTCDWAp4UG8DVGa6Akx+sRdtYHOKoBfEVO9oSp8heyTvfBTIusCOBTkShn+/VP2pVo6x2luMBLXUCrwiVqT32Alq4QM1Gt0bacU71hzf9Oo6wI4P/rwWO8/t6nNLVrBc70j7K5qhSPFhVK2O6GAFOR2Gw+vVpYLGY41TuqwydplBUBfN+GIAOj01y8NJHupmS8tl4tYpWsPQ1BAJ7tHE5zSzJbx9AEEzNRtummIWmTFQH8+g3lABxqH0pzSzJbeHKGntCkLqFPUtO6Yvy+PI526Dj4Uk5qDZS0y4oAvn29n5ICrwbwZcQzULQHnhyPR9hdH+BZnchcUjwDRX/e0icrArjXI1zXGNQAvgxNIXTO7oYArd0hpiLRdDclY53sCVMfLMLv001D0iUrAjhYwyit3WHGpyPpbkrGOt03SoHXw4Z1xeluStbbUx9kJmo41aMTmYs51RvWFZhplj0BvClINGZ0XHIJp/tG2VhZQp43a76tGWtPQwCAozqRuaCZaIwz/ZqBkm5Z80nf12hNZB68oMMoi2nTXXgc01BeRLA4n2e1w7Cg8wNjzEQN29frz1s6ZU0ALy8pYFNlCYd1HHxBkzNRLg6NawB3iIg1kal/8S3spC6hzwhZE8AB9m0o51D7sC7oWcDZ/jGM0QlMJ+1pCHCqN8zkjE5kzneqJ4xHYHOV/rylU1YF8Oubglwam+aCbnl1lbY+O6VLF1U4Znd9kEjM0NodSndTMs7J3jDNlSX48r3pbkpOy64Argt6FnWmbxSPwMbKknQ3Zc2IT2RqPvjVTvWOag2UDJBVAXxbjZ/SwjwN4Ato6xtlw7piCvO0R+SU2oCPytICHQefZ3ImyvnBMR3/zgBZFcCtBT0BDl3Q1K75TveNskU3MXZUfCJTM1GudLpvFGPQHPAMkFUBHOA5G8o50RNibEoX9MTNRGOcGxjTCUwX7G4I0tanC8jm0l14MkfWBfB9TeXEDBzp0F543IXBcSIxozUpXLCnPkDMQEuXTmTGneoNU+D10FyhK37TLesC+PX2gp7D7RrA407bGSjaA3fe7viKTB1GmXWyN8zm6lJd8ZsBsu47ECjOZ3NVCYd0ReaseBErLSPrvJoyH9X+Qs1EmeNUT5jtmq6aEbIugIOVTniofUgX9Nja+kapC/goLcxLd1PWpD0NAY7qkB0AockZukYmtQZ4hsjOAN5UztD4DOcGxtLdlIxwum+ULTqh5Jrd9UHODowRnpxJd1PSrs1eQq854Jkh4QAuIo0i8lMRaRGR4yLyR042bCmXF/RorygWM5zpH2WLLml2zZ6GAMbAcZ3I5KRdXlczUDJDMj3wCPB+Y8y1wPOB94jItc40a2lbq0vx64IeADqHJ5iciekEpot21dsrMnUik1O9YUoKvNQHi9LdFEUSAdwY022MOWTfDgOtQL1TDVuKxyPs3RDUiUwu5+RqDRT3VPkLqQv4dCIT6+dta40fj0fS3RSFQ2PgItIM7AN+7cTxVmLfhnJO9YYZzfEFPQ8e7qDMl8fOurJ0N2VN292ge2ROzkQ53jWi498ZJOkALiKlwHeBPzbGXDVIKCJ3i8gBETnQ39+f7OlmXb8haC3ouZi74+AXL43z8LEe3vRbGygu0AwUN+1pCHJuYIyRidydyPz2wQ5CkxHu2FeX7qYoW1IBXETysYL3A8aYBxd6jjHmPmPMfmPM/qqqqmROd4X4Dj25PIzy1V+eR0R46w3N6W7KmrfbHgc/nqO98Eg0xr0/O8O+DUFu2FSR7uYoWzJZKALcD7QaYz7jXJNWJlCcz9bq0pydyAxPzvDNpy/yyt211OmEkuviAfxojgbw/zjaRcfQBH9w6xasj77KBMn0wG8E3gK8WESesb9e6VC7VuT6DeUcvpibO/R860AHo1MR3vHCjeluSk4oLymgcV1RTmaixGKGf378DNtr/LxkR3W6m6PmSCYL5efGGDHG7DHG7LW/fuRk45ZzfVOQ4fEZzubYgp5ozPDVX55jf1M5exuD6W5OznhecwU/PdlH98hEupuSUj9p7eVU7yjvvnWzZp9kmKxciRn3nCZrHPzhYz1pbklqPdrSw8VLE9r7TrE/fulWojHDR3/Qmu6mpIwxhi88fobGdUW8ek9tupuj5snqAL65qpSXXVvD5x5rm82HzgX3//wcDeVF3LZzfbqbklMa1xXzhy/awg+f7eaJU85lVGWyp84O8szFYe65ebNWH8xAWf0dERH+7nW7KfPl8cfffIbpSCzdTXLdkYvDPH1+iLte0IxX/5xNuf9x8yaaK4r564eOMxVZ+7vVf+GnZ6jyF3LncxrS3RS1gKwO4ACVpYV8/HV7aO0O8dmfnEp3c1x3/8/PUVqYxxue25jupuQkX76Xv7ljF2cHxvjSk+fS3RxXHbk4zM9PD/DOF27U3eczVNYHcICXXlvDG/Y38sWfneHA+Uvpbo5rukcm+NGz3bzhuY34ffnpbk7OumVbFb+9az3/8F9tXLw0nu7muOYLj5+mzJfHm5/flO6mqEWsiQAO8L9fcy315UX8z28dWbPL67/2ywvEjOGuFzSnuyk573+/+loE4SM/aEl3U1zR1hvmx8d7eesLmrXOfAZbMwG8tDCPz7x+LxeHxvnYD9feh2p8OsI3ftPOy3eup3Gd7kWYbnXBIt73kq080tLLT0/0OXbc3tAk7/zaAT7w7SNpndP555+doSjfy9tu1EynTLZmAjjAc5vXcc/Nm/nGby7yWGtvupvjqO8e7GBkYkZTBzPIO164kc1VJfzVQ8eZnEl+QvOnJ/v47c89yRNt/XznYAf3fP2AI8ddjVjM8Kuzgzz0TBdvfF4j60oKUnp+tTprKoAD/MnLtrJjvZ8/++5RBken0t2cpJ0bGOMv/t+zfPSHrVzXGJzNfVfpV5Dn4SN37KL90jhf/NmZhI8zHYnx0R+08LavPE21v5Afve+F/H+v3c3jp/p521eedn1IcDoS42en+vnw957lho8/xhvv+xXFBV7+x02bXD2vSt6aG9wqzPPy2Tfu5fZ/+AV3feVpXrm7lr2NQfY0BChZYCzPGEPH0ATHOkc41jXC0PgM/sI8SgvzKPVZ//p9eQSKCri+KUhhXmpm4w9euMR9T5zlkZZe8j0eXruvnve9dKvWocgwL9hSyWuuq+MLj5+hMM/LK3atZ2NlyYpff35gjPf9+2GOdozwluc38eFXXYMv38uWaj/FBV7e/+0jvOX+X/PVu55HoNiZietYzHB+cIxnLg7z05P9PH6ij/BUhKJ8L7dsq+K2nTW8eEc1wWLtfWc6SWUdkf3795sDBw6k5FwPHurg84+1cX7QyhLwiLUN1L4NQa6pLaNzaIJjXSMc6wzNlgj1eoRgUT6jUxGmFhh/LC/O57X7GnjDcxvZ7sKmruPTEX52sp//++RZDrUPEyjK5y3Pb+L3X9BEtd/n+PmUM/pCk7zrXw/ObvG3vcbPy3et5+U7a7i2tuyqX7oz0RjD4zM8caqfv/z+Mbwe4ZN3Xscrdl29MOvhYz287xuH2VJdytff8TwqSgtX3b7e0CRHLg5zpGOYIxdHONIxTHjS6tVXlhbwkh013Lazhhu3VGq6YIYSkYPGmP1X3b9WA3jcpbFpjlwc5vDFYQ63D3Hk4jChyQgFXg/b1/vZVV/GzroAu+sDbF/vn/0BnonGGJuKEJ6MMDoVoXNogu8d7uSRlh5mooZ9G4K8YX8jr76uLuFZ+u6RCQ6cH+LgBeurpTtENGZoXFfEO27cyOuf26h1vrNI5/AEjxzv4eFjPTx9/hIxA43rithSVcrQ+AxD49NcGpueDZ4A+5vK+dyb9i25RdnPTvVzz9cPUB8s4oF3Pp/1gcV/mQ+OTvFs5whHO6yvZzuH6Q1ZQ4lej7BjvZ/rGoPsbQiypzHA1mq/LgjLAjkbwOeLxQzdoUmqSgspyFv9FMDg6BTfO9zJN5++SFvfKMUFXvY0BPCsYmjDGLgwOEbXyCQARflermsMsL9pHfuby3nhlkpdtpzlBken+ElrLz8+3ktfeJLy4gLWlRRQXlxg386npszHi3dUr+h7/euzg7zjawcoLvAuuP+pMdB+aZzOYavQlghsqixhT0OQ3fUBrmsMsrOuTHvYWUoDuMOMMRxqH+ZbT1/k7MDoql9fXeZjf1M5z2kq55raMvI1YKtlHLk4zKcfObloZkp1mY/rGgLsrg+yq75MF3utIRrAlVIqSy0WwLXbp5RSWUoDuFJKZSkN4EoplaU0gCulVJbSAK6UUllKA7hSSmUpDeBKKZWlNIArpVSWSulCHhHpBy4k+PJKYMDB5mQjvQZ6DXL9/UNuXoMmY0zV/DtTGsCTISIHFlqJlEv0Gug1yPX3D3oN5tIhFKWUylIawJVSKktlUwC/L90NyAB6DfQa5Pr7B70Gs7JmDFwppdSVsqkHrpRSag4N4EoplaUcD+AiUiQiPxMRr4g0i8iEiDwz52vBra7t5x5zuC3rRORREWmz/y2373+1iPytw+fKpPf9uyJyXERiIrJ/3mMfEpHTInJSRF5u31cgIk+ISFIbcGbYNfiUiJwQkaMi8j0RCc55zLVrsEhb4tflujnX4pKInLNv/8Tpc845t4jIx0TklIi0isj77Psd/wzMO2863/Mf2t9fIyKV8x671T7/cRH5mX2fa9971xljHP0C3gP8kX27GTi2wtet+LmraMsngT+3b/858An7tgCHgeI1+r6vAbYDjwP759x/LXAEKAQ2AmcAr/3YXwFvXkPX4DYgz779iTnfe1evwXLXZc59XwXuXOC5eQ6f+23AvwAe+//V9r+OfwYy6D3vs3+mzgOVc+4PAi3AhrnXws3vvdtfbgyhvBn4/mIP2r2tJ0XkkP31ggWes1NEfmP/pjwqIlvt+39vzv33ishyO7TeAXzNvv014HcAjPUdexx49erf3qIy5n0bY1qNMScXeOgO4N+NMVPGmHPAaeB59mP/z34Pycika/CIMSa+/fuvgAb7ttvXYCHLXZfHReSzInIA+CMR+aqI3Dnn8dE5t/9URJ62r83frODc7wb+1hgTAzDG9Nn/uvEZmCtt79kYc9gYc36Bh/478KAxpt1+Xt+cx9z63rvK0QBu/4m8ad7F2zznT6h/AvqAlxljrgfeAHx+gUO9C/icMWYvsB/oEJFr7OffaN8fZfkLXmOM6bZv9wA1cx47ANy0une4sAx834upBy7O+X+HfR/AMeC5CR4306/B24H/tG+7dg0Wssh1WUiBMWa/MebvlzjWbcBWrF84e4HniMjNyxx3M/AGETkgIv8Z/4Voc+wzMK+d6X7Pi9kGlNu/PA6KyO/Peczx730qOD3mUwkMz7vvjP2hA0BEAsA/ikj8g7htgeM8BXxYRBqwfmO2ichLgOcAT4sIQBFWQFgRY4wRkbk5k31A3Upfv4yMfd8rZYyJisi0iPiNMeEEDpGR10BEPgxEgAeWe64D12AhC12XhXxzBc+5zf46bP+/FCu4PbHEawqBSWPMfhF5HfBlLgdtJz8Dc6X7PS8mD+vn6CVYP0NPicivjDGnXPreu87pAD4B+JZ5zp8AvcB1WH8BTM5/gjHm30Tk18CrgB+JyD1YY3ZfM8Z8aBXt6RWRWmNMt4jUcuWH3me31wmZ9r4X0wk0zvl/g31fXOFC7VqhjLsGInIX1hDBS+whA3D3GixkJdcFYGzO7Qj2X8ci4gHik78C/J0x5t5VnL8DeNC+/T3gK3Mec/IzMFe63/NiOoBBY8wYMCYiT2D9LJ6yH3f6e+86R4dQjDFDgFdElvrmBYBue0zuLcBVY5kisgk4a4z5PNY42h7gMeBOEam2n7NORJrs2/8iIs+bfxzgIeCt9u23cuWY3DasP5uSloHvezEPAW8UkUIR2YjVk/mNfawKYMAYM7OK483KtGsgIq8APgjcbowZn/OQa9dgISu8LvOdx+opAtwO5Nu3fwy8XURKAUSkfs41eUxE6ucfCGts90X27Vu4HKzAwc/AXBnwnhfzfeCFIpInIsXAbwGt9rEc/96nghuTmI8AL1zi8S8AbxWRI8AOrvwtHPd64JiIPAPsAv7FGNMC/AXwiIgcBR4Fau3n7wG6FjjOx4GXiUgb8FL7/3EvAn644ne1vIx53yLyWhHpAG4AfigiPwYwxhwHvoU1E/8w8B5jTNR+mRPXI2OuAfCPgB941B6D/yKk5BosZLnrMt//BW6xr9MN2NfJGPMI8G9Yf/o/C3wH8Ns91i3ApQWO9XHgv9nP/zvgnXMec+v9Qhrfs4i8z/75bwCOisiX7GO1Yn3Pj2L90v6SMSb+C8zNa+Eep9NagOuBrzt93CXOVwZ8e5WvqQEey7X3vczxHgS26TVI7hqk47pg/aL7zCpf4/hnINPfczq+925/uVILRUTejjVmGV32yWkgIs8FZowxzzh83Ix+34uxswbeaIz5FweOlfPXYJHjZ9R1ceszMO8cGfWeF+P2995NWsxKKaWylNZCUUqpLKUBXCmlspQGcKWUylIawJVSKktpAFcZxa5T4eiO42KVEP2Bk8dc5nx3iciqlqhLgiV17df999W+Tq0NGsCVWoIsX/FyIXfhTo2RhTRjVdlTOUgDuEqKWKU+45sE/B8R+S/79otF5AERuU1EnhKrfOy35yyJfo5YBf8PisiP7Vo1c4/rEavE6EfF2iDiU3K5pOg99nNutXvs3xFr84YHRKxqVyLyCvu+Q8DrFmm7V0Q+LSLH7OO+177/vIh8wn7t7y7xHv7SbtMxEblPLHdiVVF8wF4BWrTYe7XvP2KvPnzPMtd5sVK8Hwduss/1J6v+Bqrslu6VRPqV3V/A87FXQwJPYi1RzscqkP9nWFXjSuzH/wz4S/vxXwJV9v1vAL5s337cPuY3gA/b990N/IV9uxCrDOpG4FZgBGvJtAerkuELsQopXcSqcyJYS+d/sEDb3421NDu+8cM6+9/zwAft25ULvYe5z7dvfx14zZz3sN++vdR7PQrcbN/+FEtsagEUAz779lbggH371oXem37lxlf2bSGkMs1BrBrNZcAUcAirB3oTVuGoa4Ff2B3jAqwgux1rKfSj9v1eoHvOMe8FvmWM+Zj9/9uAPXK54H8AK4hNA78xxnQA2PVTmoFR4Jwxps2+/1+xfgnM91Lgi8be+MEYM7euRrzU6fMXeQ8ALxKRD2IF13XAceA/5p1jwfcq1hZvQWNMvCzq14HfXqCNcfksX4pX5RgN4CopxpgZETmHNe77S6xe5YuwCg2dAx41xrxp7mtEZDdw3BhzwyKH/SVWcPx7Y8wkVi/6vcaYH887zq1YvzTiojj3Mx0vtCUs/B58WMW59htjLorIX7NwCVVhgfcqc/boXKFlS/Gq3KNj4MoJTwIfwBpqeBJrV53DWFuZ3SgiWwBEpEREtgEngSoRucG+P19Eds453v3Aj4BvibXR7I+Bd4tIvv38bSJSskR7TgDNIrLZ/v9s8BWR54lIvObFo8A99jkQkXULHGux9xAP1gP2mPidc14TxqqEyGLv1RgzDAyLSLxi33I7DC1WinfuuVSO0QCunPAkVnnXp4wxvVi9wyeNMf1YPfNviFUG9ilghzFmGivgfcKewHsGuGJ/TGPMZ7B+CXwd+BJW+ddDdqrdvSzR07Z77XdjldI9xJUbeWzg8iYGXwLasUqOHmGBbI4l3sMwVgnUY1i/YJ6e87KvAl+0h3S8S7zXtwH/ZD9PFns/tsVK8R4FovZkqE5i5hgtZqVyioh8CqvM6dF0t0WpZGkAV0qpLKWTmEplEBF5OfCJeXefM8a8Nh3tUZlNe+BKKZWldBJTKaWylAZwpZTKUhrAlVIqS2kAV0qpLPX/A6PhU7yl3x4vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
